MAKing a Solution to the Traveling Student Problem

Inspiration

We wanted to use real address data around UCI to support community planning. With strong urban patterns in a small area, we saw an opportunity to extract demographic data to drive community programs and local support

What it does

We found a solution to the "Traveling Student Problem" and also found demographic patterns about the data.

How we built it

We validated addresses with Melissa’s Global Address Verification API.

We retrieved Melissa Address Keys (MAKs) and used them with the Personator API.

We mapped out Household Income and Length of Residency

We performed statistical analysis and KMeans to find out more about specific areas of the community

Challenges we ran into

Configuring the right endpoint and parameters to extract demographics.

Resolving connectivity issues and setting up proper credentials.

Mapping and interpreting the returned demographic fields to match community needs.

Accomplishments that we're proud of

Successfully integrating Melissa APIs to build an enriched community dataset.

Automating the enrichment of multiple addresses using a clean, looped process.

Translating technical results into insights that can guide local resource planning.

What we learned

Demographic data, even from a small area, provides actionable insights.

Integrating multiple data sources with well-organized code delivers powerful community intelligence.

What's next for Melissa Data Challenge

Expand the points to include more of the wider Irvine Area.

Built With

• follium
• python